Smart Broadcasting: Do you want to be seen?

This is the repository which contains code used for conducting experiments for the following paper:

M. R. Karimi, E. Tavakoli, M. Farajtabar, L. Song, and M. Gomez-Rodriguez. Smart broadcasting: Do you want to be seen? In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2016.

This is also used as a baseline for the paper:

A. Zarezade, U. Upadhyay, H. R. Raibee, M. Gomez-Rodriguez. RedQueen: An Online Algorithm for Smart Broadcasting in Social Networks. In Proceedings of the 10th ACM International Conference on Web Search and Data Mining (WSDM), 2017.

Important Dependencies

• Python 3
• numpy
• cvxopt
• Cython

Installation

This package can be installed from the repository directly:

pip install git+https://github.com/Networks-Learning/broadcast_ref.git@master#egg=broadcast_ref

After a successful installation, the module named broadcast should be available for import in a Python shell.

Troubleshooting

If the numpy header files are not found while importing the broadcast.opt.optimizer, then:

1. Find the numpy header files location:

   import numpy as np
   np.get_include()
   

2. Launch your jupyter notebook or python shell after exporting CFLAGS:

   export CFLAGS="-I ${PATH_TO_NUMPY_INCLUDE} ${CFLAGS}"
   

If you do not want to edit this particular package, then you can also execute compile.sh once to compile the packages. That should allow you to import the package elsewhere.